Evidence map›Paper›PMID 35149894›Full record

ArticleInternational journal of biometeorology2022

Optimizing sowing window, cultivar choice, and plant density to boost maize yield under RCP8.5 climate scenario of CMIP5.

Marwa G M Ali, Mukhtar Ahmed, Mahmoud M Ibrahim, Ahmed A El Baroudy, Esmat F Ali, Mohamed S Shokr, Ali A Aldosari, Ali Majrashi, Ahmed M S Kheir

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Article in International journal of biometeorology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
15.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 40 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 4 countries.

Marwa G M AliSoil and Water Department, Faculty of Agriculture, Tanta University, Tanta, Egypt.
Mukhtar AhmedDepartment of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, 90183, Umeå, Sweden.
Mahmoud M IbrahimSoil and Water Department, Faculty of Agriculture, Tanta University, Tanta, Egypt.
Ahmed A El BaroudySoil and Water Department, Faculty of Agriculture, Tanta University, Tanta, Egypt.
Esmat F AliDepartment of Biology, College of Science, Taif University, P.O.Box 11099, Taif, 21944, Saudi Arabia.
Mohamed S ShokrSoil and Water Department, Faculty of Agriculture, Tanta University, Tanta, Egypt.
Ali A AldosariGeography Department, King Saud University, Riyadh, Saudi Arabia.
Ali MajrashiDepartment of Biology, College of Science, Taif University, P.O.Box 11099, Taif, 21944, Saudi Arabia.
Ahmed M S KheirSoils, Water and Environment Research Institute, Agricultural Research Center, Giza, Egypt. drahmedkheir2015@gmail.com.
Tanta University · EGAgricultural Research Center · EGTaif University · SAKing Saud University · SAPir Mehr Ali Shah Arid Agriculture University · PK

Funding

King Saud University RGP-275Taif University TURSP-2020/110
6 · The paper itself

Abstract

The impacts of climate change and possible adaptations to food security are a global concern and need greater focus in arid and semi-arid regions. It includes scenario of Coupled Model Intercomparison Phase 5 (CMIP-RCP8.5). For this purpose, two DSSAT maize models (CSM-CERES and CSM-IXIM) were calibrated and tested with two different maize cultivars namely Single Cross 10 (SC10) and Three Way Cross 324 (TW24) using a dataset of three growing seasons in Nile Delta. SC10 is a long-growing cultivar that is resistant to abiotic stresses, whereas TW24 is short and sensitive to such harsh conditions. The calibrated models were then employed to predict maize yield in baseline (1981-2010) and under future time slices (2030s, 2050s, and 2080s) using three Global Climate Models (GCMs) under CMIP5-RCP8.5 scenario. In addition, the use of various adaptation options as shifting planting date, increasing sowing density, and genotypes was included in crop models. Simulation analysis showed that, averaged over three GCMs and two crop models, the yield of late maturity cultivar (SC10) decreased by 4.1, 17.2, and 55.9% for the three time slices of 2030s, 2050s, and 2080s, respectively, compared to baseline yield (1981-2010). Such reduction increased with early maturity cultivar (TW24), recording 12.4, 40.6, and 71.3% for near (2030s), mid (2050s), and late century (2080s) respectively relative to baseline yield. The most suitable adaptation options included choosing a stress-resistant genotype, changing the planting date to plus or minus 30 days from baseline planting date, and raising the sowing density to 9 m

Indexed as

AcclimatizationZea maysClimate ChangeDesert ClimateGenotypeAdaptationClimate changeDSSAT modelsFood securityImpactsUncertainty

Identifiers

PMID35149894
OpenAlexW4210987321

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.